Retrieval-augmented generation (RAG) performs well for localized factual queries but struggles with complex questions requiring multi-section evidence integration. Graph-based approaches introduce relational structure, yet their practical integration into QA pipelines involves significant query-time overhead. We present a practical KG-augmented RAG (KG-RAG) design that builds a knowledge graph offline with an LLM, converts graph communities into reusable summaries, and retrieves these summaries jointly with textual evidence at query time. We compare dense RAG, pure GraphRAG, and the proposed hybrid on two benchmarks representing complementary retrieval paradigms: QASPER (intra-document reasoning over scientific papers) and ObliQA (cross-document reasoning over regulatory texts). Results show that pure GraphRAG does not consistently outperform dense retrieval, whereas the hybrid configuration systematically improves relevance, correctness, and completeness while maintaining substantially lower latency than full graph-based inference.

Preparing graduate students for effective professional communication remains a central goal of higher education, yet consistently assessing the quality of presentation slide decks - particularly in fast-growing AI/ML programs - poses significant challenges.We introduce SlideGuard, an evaluation agent that assesses slide decks against a comprehensive framework of expert-defined criteria using a visual language model.The criteria, developed in collaboration with domain experts, span visual design, narrative coherence, and argumentative structure.SlideGuard delivers explicit, interpretable justifications for its scoring decisions, and its content-hash-based caching enables efficient re-evaluation after incremental edits, reducing the time educators spend on slide deck evaluation and accelerating feedback delivery to students.We evaluate the approach on a dataset of 150 annotated slide decks and show that it detects the majority of expert-identified issues, with stronger results on structural and visual criteria and known limitations on subjective dimensions such as research quality.SlideGuard is released under the Apache 2.0 license and is available on GitHub,[https://github.com/Industrial-AI-Research-Lab/SlideGuard] including all criterion prompts, configuration files, and evaluation scripts to facilitate replication.

Online advertising is one of the most widespread ways to reach and increase a target audience for those selling products. Usually having a form of a banner, advertising engages users into visiting a corresponding webpage. Professional generation of banners requires creative and writing skills and a basic understanding of target products. The great variety of goods presented in the online market enforce professionals to spend more and more time creating new advertisements different from existing ones. In this paper, we propose a neural network-based approach for the automatic generation of online advertising using texts from given webpages as sources. The important part of the approach is training on open data available online, which allows avoiding costly procedures of manual labeling. Collected open data consist of multiple subdomains with high data heterogeneity. The subdomains belong to different topics and vary in used vocabularies, phrases, styles that lead to reduced quality in adverts generation. We try to solve the problem of identifying existed subdomains and proposing a new ensemble approach based on exploiting multiple instances of a seq2seq model. Our experimental study on a dataset in the Russian language shows that our approach can significantly improve the quality of adverts generation.